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Josep Llados, Gemma Sanchez and Enric Marti. 1998. A string based method to recognize symbols and structural textures in architectural plans. Graphics Recognition Algorithms and Systems Second International Workshop, GREC' 97 Nancy, France, August 22–23, 1997 Selected Papers. Springer Link, 91–103. (LNCS.)
Abstract: This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clustering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the similarity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion.
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Josep Llados and Enric Marti. 1995. Interpretacio de dibuixos lineals mitjançant tècniques d isomorfisme entre grafs. Trobada de Joves Investigadors.
Abstract: L’anàlisi de documents té com a objectiu la interpretació automàtica de documents impresos sobre paper, amb la finalitat d’obtenir una descripció simbòlica d’aquests, que permeti el seu emmagatzemament i posterior tractament computacional. Les tècniques basades en grafs relacionals d’atributs permeten representar de manera compacta la informació continguda en dibuixos lineals i mitjançant mecanismes d’isomorfisme entre grafs, reconèixer-hi certes estructures i d’aquesta manera, interpretar el document. En aquest treball es dóna una visió general de les tènciques de grafs aplicades al reconeixement visual d’objectes en problemes d’anàlisi de documents. Aquestes tècniques s’il·lustren amb un exemple de reconeixement de plànols dibuixats a mà alçada. Finalment es proposa la utilització de tècniques de Hough com a mecanisme per accelerar el procés de reconeixement aplicant un cert coneixement sobre el domini en el que es treballa
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Josep Llados, Jaime Lopez-Krahe and Enric Marti. 1996. Hand drawn document understanding using the straight line Hough transform and graph matching. Proceedings of the 13th International Pattern Recognition Conference (ICPR’96). Vienna , Austria, 497–501.
Abstract: This paper presents a system to understand hand drawn architectural drawings in a CAD environment. The procedure is to identify in a floor plan the building elements, stored in a library of patterns, and their spatial relationships. The vectorized input document and the patterns to recognize are represented by attributed graphs. To recognize the patterns as such, we apply a structural approach based on subgraph isomorphism techniques. In spite of their value, graph matching techniques do not recognize adequately those building elements characterized by hatching patterns, i.e. walls. Here we focus on the recognition of hatching patterns and develop a straight line Hough transform based method in order to detect the regions filled in with parallel straight fines. This allows not only to recognize filling patterns, but it actually reduces the computational load associated with the subgraph isomorphism computation. The result is that the document can be redrawn by editing all the patterns recognized
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Josep Llados, Enric Marti and Jaime Lopez-Krahe. 1999. A Hough-based method for hatched pattern detection in maps and diagrams. Proceeding of the Fifth Int. Conf. Document Analysis and Recognition ICDAR ’99.479–482.
Abstract: A hatched area is characterized by a set of parallel straight lines placed at regular intervals. In this paper, a Hough-based schema is introduced to recognize hatched areas in technical documents from attributed graph structures representing the document once it has been vectorized. Defining a Hough-based transform from a graph instead of the raster image allows to drastically reduce the processing time and, second, to obtain more reliable results because straight lines have already been detected in the vectorization step. A second advantage of the proposed method is that no assumptions must be made a priori about the slope and frequency of hatching patterns, but they are computed in run time for each hatched area.
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Josep Llados, Enric Marti and Juan J.Villanueva. 2001. Symbol recognition by error-tolerant subgraph matching between region adjacency graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1137–1143.
Abstract: The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.
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Gemma Sanchez, Josep Llados and Enric Marti. 1997. Segmentation and analysis of linial texture in plans. Intelligence Artificielle et Complexité.. Paris.
Abstract: The problem of texture segmentation and interpretation is one of the main concerns in the field of document analysis. Graphical documents often contain areas characterized by a structural texture whose recognition allows both the document understanding, and its storage in a more compact way. In this work, we focus on structural linial textures of regular repetition contained in plan documents. Starting from an atributed graph which represents the vectorized input image, we develop a method to segment textured areas and recognize their placement rules. We wish to emphasize that the searched textures do not follow a predefined pattern. Minimal closed loops of the input graph are computed, and then hierarchically clustered. In this hierarchical clustering, a distance function between two closed loops is defined in terms of their areas difference and boundary resemblance computed by a string matching procedure. Finally it is noted that, when the texture consists of isolated primitive elements, the same method can be used after computing a Voronoi Tesselation of the input graph.
Keywords: Structural Texture, Voronoi, Hierarchical Clustering, String Matching.
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Gemma Sanchez and 6 others. 2003. A system for virtual prototyping of architectural projects. Proceedings of Fifth IAPR International Workshop on Pattern Recognition.65–74.
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Ernest Valveny and Enric Marti. 2003. A model for image generation and symbol recognition through the deformation of lineal shapes. PRL, 24(15), 2857–2867.
Abstract: We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents.
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Ernest Valveny and Enric Marti. 2001. Learning of structural descriptions of graphic symbols using deformable template matching. Proc. Sixth Int Document Analysis and Recognition Conf.455–459.
Abstract: Accurate symbol recognition in graphic documents needs an accurate representation of the symbols to be recognized. If structural approaches are used for recognition, symbols have to be described in terms of their shape, using structural relationships among extracted features. Unlike statistical pattern recognition, in structural methods, symbols are usually manually defined from expertise knowledge, and not automatically infered from sample images. In this work we explain one approach to learn from examples a representative structural description of a symbol, thus providing better information about shape variability. The description of a symbol is based on a probabilistic model. It consists of a set of lines described by the mean and the variance of line parameters, respectively providing information about the model of the symbol, and its shape variability. The representation of each image in the sample set as a set of lines is achieved using deformable template matching.
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Ernest Valveny and Enric Marti. 2000. Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition. Graphics Recognition Recent Advances, 1941, 193–208.
Abstract: We describe a method for hand-drawn symbol recognition based on deformable template matching able to handle uncertainty and imprecision inherent to hand-drawing. Symbols are represented as a set of straight lines and their deformations as geometric transformations of these lines. Matching, however, is done over the original binary image to avoid loss of information during line detection. It is defined as an energy minimization problem, using a Bayesian framework which allows to combine fidelity to ideal shape of the symbol and flexibility to modify the symbol in order to get the best fit to the binary input image. Prior to matching, we find the best global transformation of the symbol to start the recognition process, based on the distance between symbol lines and image lines. We have applied this method to the recognition of dimensions and symbols in architectural floor plans and we show its flexibility to recognize distorted symbols.
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